Abstract
The application of multiple UAVs in complicated tasks has been widely explored in recent years. Due to the advantages of flexibility, cheapness and consistence, the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV. Accordingly, several constraints should be satisfied to realize the efficient cooperation, such as special time-window, variant equipment, specified execution sequence. Hence, a proper task allocation in UAVs is the crucial point for the final success. The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics. To this end, a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints. In addition, four optimization objectives: completion time, target reward, UAV damage, and total range, are introduced to evaluate various allocation plans. Subsequently, to efficiently solve the multi-objective optimization problem, an improved multi-objective quantum-behaved particle swarm optimization (IMOQPSO) algorithm is proposed. During this algorithm, a modified solution evaluation method is designed to guide algorithmic evolution; both the convergence and distribution of particles are considered comprehensively; and boundary solutions which may produce some special allocation plans are preserved. Moreover, adaptive parameter control and mixed update mechanism are also introduced in this algorithm. Finally, both the proposed model and algorithm are verified by simulation experiments.
摘要
**年来,关于多无人机在复杂任务中的应用有了广泛的探索。无人机具有部署灵活、成本低廉 和自持力**的优点,合理的协同方案可以实现无人机之间的高效信息融合与资源互补,突破单架无人 机能力限制,提升任务执行效率。在现实应用中,异构任务分配需要满足多种现实约束,如飞行**台 差异、任务时间窗、任务耦合关系等。因此,作为无人机集群应用的顶层规划,异构无人机任务分配 可以建模为基于无人机动力学约束的多目标优化问题。在问题模型构建中,设计了多层编码策略和约 束调度方法来处理多种耦合约束,引入了任务时间、任务收益、无人机损耗和飞行航程四个优化目标 对分配方案进行综合评估。为高效求解这一高维多目标优化问题,本文提出了一种改进的多目标量子 粒子群算法,即利用改进的非支配解评估方法来引导算法进化,综合考虑解的收敛性和分布性,并保 留了合理的边界解。仿真结果验证了构建模型的可行性与改进算法的有效性。
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References
EUN Y, BANG H. Cooperative task assignment/path planning of multiple unmanned aerial vehicles using genetic algorithm [J]. Journal of Aircraft, 2009, 46(1): 338–343. DOI: 10.2514/1.38510.
DENG Qi-bo, YU Jian-qiao, WANG Ning-fei. Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes [J]. Chinese Journal of Aeronautics, 2013, 26(5): 1238–1250. DOI: 10.1016/j.cja.2013.07.009.
ZHAI Zhao-yu, ORTEGA J M, MARTINEZ N L, RODRIGUEZMOLINA J. A mission planning approach for precision farming systems based on multi-objective optimization [J]. Sensors, 2018, 18(6): 1795. DOI: 10.3390/s18061795.
CHEN Hao-xiang, NAN Ying, YANG Yi. Multi-UAV reconnaissance task assignment for heterogeneous targets based on modified symbiotic organisms search algorithm [J]. Sensors, 2019, 19(3): 734. DOI: 10.3390/s19030734.
BUCKMAN N, CHOI H L, HOW J P. Partial replanning for decentralized dynamic task allocation [C]// AIAA Scitech 2019 Forum. San Diego: AIAA, 2019: 0915.
EDISON E, SHIMA T. Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms [J]. Computers & Operations Research, 2011, 38(1): 340–356. DOI: 10.1016/j.cor. 2010.06.001.
LOZANO Á, CARIDAD J, DE J P, VILLARRUBIA G G, BAJO J. Smart waste collection system with low consumption lorawan nodes and route optimization [J]. Sensors, 2018, 18(5): 1465. DOI: 10.3390/s18051465.
JIANG Jun, NG K M, POH K L, TEO K M. Vehicle routing problem with a heterogeneous fleet and time windows [J]. Expert Systems with Applications, 2013, 41(8): 3748–3760. DOI: 10.1016/j.eswa.2013.11.029.
SAVLA K, FRAZZOLI E, BULLO F. Traveling salesperson problems for the dubins vehicle [J]. IEEE Transactions on Automatic Control, 2008, 53(6): 1378–1391. DOI: 10.1109/ TAC.2008.925814.
ZHAO Zhe, YANG Jian, NIU Yi-feng, ZHANG Yu, SHEN Lin-cheng. A hierarchical cooperative mission planning mechanism for multiple unmanned aerial vehicles [J]. Electronics, 2019, 8(4): 443. DOI: 10.3390/ electronics8040443.
WANG Zhu, LIU Li, LONG Teng, WEN Yong-lu. Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double- chromosome encoding [J]. Chinese Journal of Aeronautics, 2018, 31(2): 339–350. DOI: 10.1016/j.cja.2017.09.005.
SCHWARZROCK J, ZACARIAS I, BAZZAN A L C, MOREIRA L H, FREITAS E P D. Solving task allocation problem in multi unmanned aerial vehicles systems using swarm intelligence [J]. Engineering Applications of Artificial Intelligence, 2018, 72: 10–20. DOI: 10.1016/j.engappai. 2018.03.008.
WANG **g-**g, ZHANG Y F, GENG L, FUH J Y H, TEO S H. A heuristic mission planning algorithm for heterogeneous tasks with heterogeneous uavs [J]. Unmanned Systems, 2015, 3(3): 205–219. DOI: 10.1142/S23013850155 00132.
SHIMA T, RASMUSSEN S, SHIMA T, RASMUSSEN S. Uav cooperative decision and control: Challenges and practical approaches [M]. Society for Industrial and Applied Mathematics, 2008. DOI: 10.1137/1.97808987 18584.
DE A, KUMAR S K, GUNASEKARAN A, TIWARI M K. Sustainable maritime inventory routing problem with time window constraints [J]. Engineering Applications of Artificial Intelligence, 2017, 61: 77–95. DOI: 10.1016/ j.engappai.2017.02.012.
KUO R J, CHENG W C. Hybrid meta-heuristic algorithm for job shop scheduling with due date time window and release time [J]. International Journal of Advanced Manufacturing Technology, 2013, 67(1-4): 59–71. DOI: 10.1007/s00170-013-4753-z.
SCHUMACHER C, CHANDLER P, PACHTER M, PACHTER L. Uav task assignment with timing constraints via mixed-integer linear programming [C]// AIAA 3rd Unmanned Unlimited Technical Conference. 2004: 6410. DOI: 10.2514/6.2004-6410.
SCHUMACHER C, CHANDLER P R, PACHTER M, PACHTER L S. Optimization of air vehicles operations using mixed-integer linear programming [J]. Journal of the Operational Research Society, 2007, 58(4): 516–527. DOI: 10.1057/palgrave.jors.2602176.
SINGH M R, MAHAPATRA S S. A quantum behaved particle swarm optimization for flexible job shop scheduling [M]. Pergamon Press, 2016. DOI: 10.1016/j.cie.2015.12.004.
BUI K H, JUNG J, CAMACHO D. Consensual negotiationbased decision making for connected appliances in smart home management systems [J]. Sensors, 2018, 18(7): 2206. DOI: 10.3390/s18072206.
PEREZ-CARABAZA S. BESADA-PORTAS E, LOPEZOROZCO J A, JESUS M. Ant colony optimization for multiuav minimum time search in uncertain domains [J]. Applied Soft Computing, 2018, 62: 789–806. DOI: 10.1016/j.asoc. 2017.09.009.
MILORADOVIC B, ÇÜRÜKLÜ B, EKSTRÖM M. A genetic planner for mission planning of cooperative agents in an underwater environment [C]// IEEE Symposium Series on Computational Intelligence. Athens: IEEE, 2016: 1–8. DOI: 10.1109/SSCI. 2016.7850163.
SUN Jun, FENG Bin, XU Wen-bo. Particle swarm optimization with particles having quantum behavior [C]// Congress on Evolutionary Computation. Portland: IEEE, 2004: 325–331. DOI: 10.1109/CEC.2004.1330875.
CHANDLER P, SPARKS A. Decentralized control for an autonomous team [C]// AIAA Unmanned Unlimited Conference 2013. San Diego: AIAA, 2013. DOI: 10.2514/6.2003-6571.
CHEN Qing-yang, LU Ya-fei, JIA Gao-wei, LI Yue, ZHU Bing-jie, LIN Jun-can. Path planning for uavs formation reconfiguration based on dubins trajectory [J]. Journal of Central South University, 2018, 25: 2664–2676. DOI: 10.1007/s11771-018-3944-z.
LIN Qiu-zhen, LIU Song-bai, ZHU Qing-ling, TANG Chao-yu, SONG Rui-zhen, COELLO C A C, WONG K C, ZHANG Jun. Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems [J]. IEEE Transactions on Evolutionary Computation, 2016, 22(1): 32–46. DOI: 10.1109/TEVC.2016.2631279.
WANG Rui, PURSHOUSE R C, FLEMING P J. Preferenceinspired coevolutionary algorithms for many-objective optimization [J]. IEEE Transactions on Evolutionary Computation, 2013, 17(4): 474–494. DOI: 10.1109/TEVC. 2012.2204264.
LI Mi-qing, YANG Sheng-xiang, LIU **ao-hui. Shift-based density estimation for pareto-based algorithms in many-objective optimization [J]. IEEE Transactions on Evolutionary Computation, 2014, 18(3): 348–365. DOI: 10.1109/TEVC.2013.2262178.
YANG Jun-jie, ZHOU Jian-zhong, FANG Reng-cun, LI Ying-hai, LIU Li. Multi-objective particle swarm optimization based on adaptive grid algorithms [J]. Journal of System Simulation, 2008, 20(21): 5843–5847. DOI: 10.1088/0953-8984/20/42/425208.
AL M N, PETROVSKI A, MCCALL J. D2MOPSO: Mopso based on decomposition and dominance with archiving using crowding distance in objective and solution spaces [J]. Evolutionary Computation, 2014, 22(1): 47–77. DOI: 10.1162/EVCO_a_00104.
XU Ming-ming, ZHANG Liang-pei, DU Bo, ZHANG Le-fei, FAN Yan-guo, SONG Dong-mei. A mutation operator accelerated quantum-behaved particle swarm optimization algorithm for hyperspectral endmember extraction [J]. Remote Sensing, 2017, 9(3): 197. DOI: 10.3390/rs9030197.
FAN Zhun, LI Wen-ji, CAI **n-ye, WEI Cai-min, ZHANG Qing-fu, DEB K, GOODMAN E D. Push and pull search for solving constrained multi-objective optimization problems [J]. Swarm and Evolutionary Computation, 2017, 44: 665–679. DOI: 10.1016/j.swevo.2018.08.017.
COELLO C A C, LECHUGA M S. MOPSO: A proposal for multiple objective particle swarm optimization [C]// Congress on Evolutionary Computation. 2002: 1051–1056. DOI: 10.1109/CEC.2002.1004388.
PENG Guang, FANG Yang-wang, PENG Wei-shi, CHAI Dong, XU Yang. Multi-objective particle optimization algorithm based on sharing-learning and dynamic crowding distance [J]. Optik, 2016, 127(12): 5013–5020. DOI: 10.1016/j.ijleo.2016.02.045.
ZHANG Qing-fu, LI Hui. MOEA/D: A multiobjective evolutionary algorithm based on decomposition [J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712–731. DOI: 10.1109/TEVC.2007.892759.
DEB K, PRATAP A, AGARWAL S, MEYARIVAN T. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182–197. DOI: 10.1109/4235.996017.
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Foundation item: Project(61801495) supported by the National Natural Science Foundation of China
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Wang, Jf., Jia, Gw., Lin, Jc. et al. Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm. J. Cent. South Univ. 27, 432–448 (2020). https://doi.org/10.1007/s11771-020-4307-0
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DOI: https://doi.org/10.1007/s11771-020-4307-0
Key words
- unmanned aerial vehicles
- cooperative task allocation
- heterogeneous
- constraint
- multi-objective optimization
- solution evaluation method